The Electoral Map: Clinton Vs. Trump [Greg Laden's Blog]

This uses the technique previously described. However, instead of using RCP averages for all polled states and then using extreme (non-tossup) states to develop the regression model, this method uses only polling from states with one or more recent poll, and only with good polls. these poll numbers are then “predicted” by black/hispanic/white/Voted_Romney numbers, and that generates a model, based on just over 20 states, designed to predict all the states.

As expected, the r-squared value is much lower using this method, but this method does not violate any important statistical laws like the last one did.

Most of the polling data pre-dates the revelation of Trump’s interest in sexual assault, last Friday, and of course, Monday’s “I’ll throw my opponent in prison when I win” debate on Sunday. If you believe those events influence the election further, then you can figure this is a conservative estimate from the perspective of Clinton.

All of the blue states, both shades, are projected to go to Clinton, but I left the three closest to 50-50 in light blue.

I suspect the most controversial state here is actually Iowa, which seems to be throwing some sort of hissyfit in the polls.

And this, of course, is why my model is different from everyone else’s. The polls are used in this case to calibrate (in the absence of earlier results, like could be done in the primary!) but the actual prediction then does not use the polls directly. So, even though a recent poll showing Iowa as Trump, the model does not, because the model does not lie like the Iowans do, apparently!